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Inspired by recent work of Farb, Kisin and Wolfson, we develop a method for using actions of finite group schemes over a mixed characteristic dvr $\mathbb{R}$ to get lower bounds for the essential dimension of a cover of a variety over $K = \mathrm{Frac}(\mathbb{R})$. We then apply this to prove $p$-incompressibility for congruence covers of a class of unitary Shimura varieties for primes $p$ at which the reduction of the Shimura variety (at any prime of the reflex field over $p$) does not have any ordinary points. We also make some progress towards a conjecture of Brosnan on the $p$-incompressibility of the multiplication by $p$ map of an abelian variety., DOCUMENTA MATHEMATICA, Vol 27 (2022), p. 151-182

In the initial phases of the software development cycle, domain modelling is typically performed to transform informal requirements expressed in natural language into concise and analyzable domain models. These models capture the key concepts of an application domain and their relationships in the form of class diagrams. Building domain models manually is often a time-consuming and labor-intensive task. The current approaches which aim to extract domain models automatically, are inadequate in providing insights into the modelling decisions taken by extractor systems. This inhibits modellers to quickly confirm the completeness and conciseness of extracted domain models. To address these challenges, we present DoMoBOT, a domain modelling bot that uses a traceability knowledge graph to enable traceability of modelling decisions from extracted domain model elements to requirements and vice-versa. In this tool demo paper, we showcase how the implementation and architecture of DoMoBOT facilitate modellers to extract domain models and gain insights into the modelling decisions taken by our bot.

The models and data framework demystifies the different roles that models and data play in software development and operation and clarifies where machine learning and artificial intelligence techniques could be used.

Domain modelling transforms domain problem descriptions written in natural language (NL) into analyzable and concise domain models (class diagrams) during requirements analysis or the early stages of design in software development. Since the practice of domain modelling requires time in addition to modelling skills and experience, several approaches have been proposed to automate or semi-automate the construction of domain models from problem descriptions expressed in NL. Despite the existing work on domain model extraction, some significant challenges remain unaddressed: (i) the extracted domain models are not accurate enough to be used directly or with minor modifications in software development, (ii) existing approaches do not facilitate the tracing of the rationale behind the modelling decisions taken by the model extractor, and (iii) existing approaches do not provide interactive interfaces to update the extracted domain models. Therefore, in this paper, we introduce a domain modelling bot called DoMoBOT, explain its architecture, and implement it in the form of a web-based prototype tool. The bot automatically extracts a domain model from a problem description written in NL with an accuracy higher than existing approaches. Furthermore, the bot enables modellers to update a part of the extracted domain model and in response the bot re-configures the other parts of the domain model pro-actively. To improve the accuracy of extracted domain models, we combine the techniques of Natural Language Processing and Machine Learning. Finally, we evaluate the accuracy of the extracted domain models.

Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges.

Collaborative modelling has become a necessity when developing a complex system or in a team of modellers with a diverse set of expertise. Textual notations have a long history in software engineering because of their fast editing style, simple usage, and scalability. Therefore, we propose a novel collaborative modelling framework for the graphical User Requirements Notation (URN) which we call tColab. It uses the text-based TGRL (Textual Goal-oriented Requirement Language) to build URN goal models and then automatically generates corresponding graphical models. This framework is based on the architecture of Eclipse Che and Theia. On one side, Theia provides support for LSP (Language Server Protocol) so that textual models can be built and their corresponding graphical models can be generated in a browser IDE (Integrated Development Environment). On the other hand, Eclipse Che adds support for collaboration where multiple modellers can contribute to building the textual models in an online collaborative manner. This initiative aims to replace the jUCMNAV tool, which is the most comprehensive URN modelling tool to date but only supports a single user.

Background: In rural India, snakebite is an occupational and environmental hazard. WHO listed snakebite as one of the neglected tropical diseases and in rural India it is underreported because of popular traditional methods of treatments and lack of healthcare facilities. This study is an attempt to look into the epidemiological, clinical and prognostic indicators of neurotoxic snake bite in rural India.Methods: This study is a prospective, descriptive study of 25 neurotoxic snake bites admitted in our hospital from January to September 2019. Clinical factors like age, sex, clinical features, management and the outcome of patients are studied.Results: Young males were common victims of snakebite in evening hours during monsoon season. All patients received traditional methods of treatment, and despite this 64% of our patients reported to the hospital within 6 hours after the bite. Overall, the mortality rate in this study was 12%. Poor outcome was also noted in cases who were having respiratory failure and needed artificial ventilation (30%). Mortality was 22.2% in patients where bite to admission time was greater than 6 hours as compared to 6.25% in patients where it was less than 6 hours.Conclusions: Author concluded that a neurotoxic snake bite is a life-threatening emergency. In preventive measures, outdoor activities in dim lights during monsoon season should be restricted. Traditional treatments are still popular and trusted methods of treatment in rural India and these healers can be educated to recognize and refer immediately all neurotoxic bites to nearby medical facilities.

Use of mesh for reinforcing hernia defects has become standard procedure but it is associated with a few serious complications like bowel erosion and fistula formation. We present a case of a 62 yrs lady with enterocutaneous fistula due to mesh erosion of small bowel, 10 yrs after open incisional hernia repair using polypropylene mesh who had to undergo laparotomy and resection of eroded bowel. A brief review of literature revealed that very few case reports of such fistulas following open incional hernioplasty have been reported and that current research on improving the properties of mesh may reduce such complications in future.

Background: Infertility is a multidimensional health issue which is rising dramatically. The common causes include ovarian, uterine, tubal disorders, hormonal imbalance, age-related factors and lifestyle factors. The low economic strata poses a subset of problems like difficulty in seeking healthcare, treatment costs and poor compliance. Authors sought to evaluate the factors for primary and secondary infertility in women of reproductive age group who belong to low socio-economic strata using laparoscopy.Methods: A prospective observational study was conducted in the obstetrics and gynaecology department at Sanjay Gandhi memorial hospital, Delhi comprising 50 infertile women of reproductive age group belonging to low socioeconomic class for a period of 2 years from June 2015 onwards.Results: Among primary infertility, tuberculosis (27.02%), ovarian cyst (16.22%), adhesions (10.81%), polycystic ovaries (10.81%) and Pelvic inflammatory disease (10.81%) were the major findings whereas in secondary infertility Pelvic inflammatory disease (23.07%), tuberculosis (15.38%), adhesions (15.38%) and endometriosis (7.69%) were the major factors seen in the study.Conclusions: Tuberculosis and pelvic inflammatory disease were the major factors seen in infertile women of low socioeconomic status thus, they should be kept high on the list of differential diagnosis even if the investigative work up is negative.

Background: Transurethral Resection of Prostate (TURP) has been gold standard of surgical treatment of BPH but nonetheless it is associated with many complications. Transurethral Electro-Vaporisation of Prostate (TUEVP) is a new promising modality which has similar results and better side effect profile.Methods: A prospective, randomised, comparative study was conducted on seventy patients with symptomatic BPH in the Surgery department of Sanjay Gandhi Memorial Hospital, Delhi over two years starting from December 2014. These patients were randomly assigned to two groups- A and B using online random number generator. Patients of group A underwent TUEVP and those of group B underwent TURP. Overall patient satisfaction, patient safety, procedural efficacy and operative ease of the surgeon were compared.Results: Mean operative time (42.1 min in TUEVP and 38.4 min in TURP) and complication rates (14.3% in TUEVP and 11.4% in TURP) were comparable in both groups. Intra-operative bleeding was significantly less (2.9% in TUEVP; 22.9% in TURP) and visual clarity of operative field was significantly better in TUEVP (persistently clear in 97.1% in TUEVP; 77.1% in TURP). The mean catheterisation time (1.14 days in TUEVP and 2.51 days in TURP) and hospitalisation time (2.14 days in TUEVP and 3.1 days in TURP) were significantly shorter in TUEVP.Conclusions: TUEVP is comparable to TURP in terms of patient satisfaction, safety, operative time and procedural efficacy. TUEVP has shorter duration of catheterisation and hospitalisation and is better than TURP in terms of intra-operative bleeding and operative ease of the surgeon.